Instructions to use sieg2011/codet5-base-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sieg2011/codet5-base-sql-create-context with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sieg2011/codet5-base-sql-create-context")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sieg2011/codet5-base-sql-create-context")
model = AutoModelForSeq2SeqLM.from_pretrained("sieg2011/codet5-base-sql-create-context")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sieg2011/codet5-base-sql-create-context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sieg2011/codet5-base-sql-create-context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sieg2011/codet5-base-sql-create-context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sieg2011/codet5-base-sql-create-context

SGLang

How to use sieg2011/codet5-base-sql-create-context with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sieg2011/codet5-base-sql-create-context" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sieg2011/codet5-base-sql-create-context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sieg2011/codet5-base-sql-create-context" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sieg2011/codet5-base-sql-create-context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sieg2011/codet5-base-sql-create-context with Docker Model Runner:
```
docker model run hf.co/sieg2011/codet5-base-sql-create-context
```

sieg2011 commited on May 3, 2025

Commit

04d7cfc

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1 Parent(s): 585c963

Update README.md

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@@ -6,9 +6,14 @@ tags:
 - generated_from_trainer
 metrics:
 - bleu
 model-index:
 - name: codet5-base-sql-create-context
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -64,4 +69,4 @@ The following hyperparameters were used during training:
 - Transformers 4.50.3
 - Pytorch 2.5.1+cu121
 - Datasets 3.5.0
-- Tokenizers 0.21.1

 - generated_from_trainer
 metrics:
 - bleu
+- rouge
+- exact_match
 model-index:
 - name: codet5-base-sql-create-context
   results: []
+datasets:
+- b-mc2/sql-create-context
+pipeline_tag: text2text-generation
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.50.3
 - Pytorch 2.5.1+cu121
 - Datasets 3.5.0
+- Tokenizers 0.21.1